No abstract is provided for this article.
No abstract is provided for this article.
This paper introduces a dual-function radar-communication (DFRC) system with cognitive radio capability to tackle the spectral scarcity problem in wireless communications. Particularly, a cognitive DFRC system operates on a spectrum owned by a primary system to simultaneously perform data communication and target tracking with the condition that its interference to the primary users (PUs) is below a certain threshold. To achieve this, an optimization problem is formulated to jointly design the beamforming vectors for both the radar and communication functions in such a way that the mean square error (MSE) of the beam pattern between the designed and desired waveforms is minimized. The optimization problem has the following three constraints: i) the signal-to-interference-plus-noise ratio (SINR) at each data communication user is above a predetermined level; ii) the per-antenna transmit power is maintained at a given level; iii) the interference imposed on each PU is below a certain threshold. Both the semidefinite relaxation and nature-inspired firefly algorithms are proposed in order to search for the optimal solutions to the optimization problem. The simulation results indicate that our proposed algorithms can enable the DFRC system to protect the PUs while simultaneously performing its communication and radar functions.
Design optimization in engineering tends to be very challenging, partly due to the complexity and high nonlinearity of the problem of interest and partly due to stringent design codes in engineering. Conventional algorithms are not the best tools for highly nonlinear global optimization, as they are local search algorithms and thus often miss the global optimality. In addition, design solutions have to be robust and subject to uncertainty in the parameters and tolerance of available components and materials. Metaheuristic algorithms have become increasingly popular in the last two decades. This chapter reviews some of the latest metaheuristics.KeywordsAlgorithm, ant algorithm, bee algorithm, bat algorithm, cuckoo search, firefly algorithm, harmony search, particle swarm optimization, metaheuristics
There is a growing interest in discovering Internet path characteristics using end-to-end measurements. However, the current mechanisms for performing this task either send probe traffic, or require the sender to cooperate by time stamping the packets or sending them back-to-back. Furthermore, most of these techniques require the packets to carry sequence numbers to detect losses, and a few of them assume the existence of multicast. This paper introduces a completely passive approach for learning Internet path characteristics. In particular, we show that by noting the time difference between consecutive packets, a passive observer can cluster the flows into groups, such that all the flows in one group share the same bottleneck. Our approach relies on the observation that the correct clustering minimizes the entropy of the inter-packet spacing seen by the observer. It does not inject any probe traffic into the network, does not require any cooperation from the senders, and works with any type of traffic whether it is TCP, UDP, or even multicast.
No abstract is provided for this article.
No abstract is provided for this article.
No abstract is provided for this article.
No abstract is provided for this article.
Enzyme reactions with inhibition and cooperativity are modelled in terms of a pair of coupled nonlinear reaction–diffusion equations. The governing equations are solved using stochastic cellular automata with local rules derived from the corresponding nonlinear partial differential equations. The parallel cellular automaton is implemented using domain decomposition according to the nature of the locality of its update rules. Numerical simulations show stable 2-D and 3-D pattern formation, and complex patterns have the interesting feature of self-organized criticality. The numerical results of cellular automata are also compared with results obtained from finite difference and finite element methods.
Traditional K-means clustering algorithms have the drawback of getting stuck at local optima that depend on the random values of initial centroids. Optimization algorithms have their advantages in guiding iterative computation to search for global optima while avoiding local optima. The algorithms help speed up the clustering process by converging into a global optimum early with multiple search agents in action. Inspired by nature, some contemporary optimization algorithms which include Ant, Bat, Cuckoo, Firefly, and Wolf search algorithms mimic the swarming behavior allowing them to cooperatively steer towards an optimal objective within a reasonable time. It is known that these so-called nature-inspired optimization algorithms have their own characteristics as well as pros and cons in different applications. When these algorithms are combined with K-means clustering mechanism for the sake of enhancing its clustering quality by avoiding local optima and finding global optima, the new hybrids are anticipated to produce unprecedented performance. In this paper, we report the results of our evaluation experiments on the integration of nature-inspired optimization methods into K-means algorithms. In addition to the standard evaluation metrics in evaluating clustering quality, the extended K-means algorithms that are empowered by nature-inspired optimization methods are applied on image segmentation as a case study of application scenario.
No abstract is provided for this article.
For any metaheuristic algorithm, it is important to balance exploration and exploitation because the interaction of these two key components can significantly affect the efficiency. However, how to achieve a fine balance is still an open problem. We attempt to explore this challenging issue by using eagle strategy in combination with the recently developed flower algorithm. Our results on test benchmarks suggest that more effort should focus on explorative search for multimodal problems.
Random walks and other stochastic components are an intrinsic part of nature-inspired metaheursitic algorithms. They are often used as random numbers and randomization techniques in metaheuristic algorithms, and the efficiency of a metaheuristic algorithm may implicitly depend on the appropriate use of such randomization. In this chapter, we first introduce the fundamental ideas of random variables and theory of random walks and Lévy flights. Then, we discuss the relationship between optimization, random walks and Markov chains, followed by the analysis of step sizes and efficiency of an algorithm using the framework of Markov chain theory.
Nature-inspired algorithms such as Particle Swarm Optimization and Firefly Algorithm are among the most powerful algorithms for optimization. In this paper, we intend to formulate a new metaheuristic algorithm by combining Levy flights with the search strategy via the Firefly Algorithm. Numerical studies and results suggest that the proposed Levy-flight firefly algorithm is superior to existing metaheuristic algorithms. Finally implications for further research and wider applications will be discussed.
Proper heat transfer management is important to key electronic components in microelectronic applications. Pulsating heat pipes (PHP) can be an efficient solution to such heat transfer problems. However, mathematical modelling of a PHP system is still very challenging, due to the complexity and multiphysics nature of the system. In this work, we present a simplified, two-phase heat transfer model, and our analysis shows that it can make good predictions about startup characteristics. Furthermore, by considering parameter estimation as a nonlinear constrained optimization problem, we have used the firefly algorithm to find parameter estimates efficiently. We have also demonstrated that it is possible to obtain good estimates of key parameters using very limited experimental data.
In a tower test of Newton's inverse-square law of gravitation we found no evidence for the non-Newtonian force, and the accuracy of the experiment constrains the Yukawa potential coupling constant |α| to be less than 0.0005.
No abstract is provided for this article.
Probabilistic components are essential in nature-inspired algorithms because almost all metaheuristic algorithms use some forms of randomization and random initialization in terms of uniform distributions. This chapter introduces the fundamentals of probability distributions, Markov chains, Monte Carlo, and random walks. These concepts will form the foundations for algorithm analysis.
